Techno-Economic Analysis of Digital Tools in Financial and Risk Advisory Services

Abstract

The digital transformation of financial services has greatly revolutionized the way financial institutions evaluate, deal and counsel risk. With the process of financial advice and risk of credit assessment becoming more and more intertwined with the use of sophisticated analysis technologies, there is an ever-increasing necessity to comprehend the technological efficiency, as well as the economic proliferation, of these digital instruments. This study has provided a techno-economic analysis of machine-learning-based decision-support systems on the Credit Risk Loan Eligibility dataset. The experiment assesses the effectiveness of digital analytical applications using logistic regression, random forests, gradient boosting, and AutoML pipelines to improve the accuracy, speed, and consistency of assessing credit risk relative to traditional advisory processes. Technologically, this study investigates the accuracy of models, AUC-ROC, and dynamics of precision-recall, and feature contribution of risk prediction. On the economic front, the paper measures the economic consequences of better predictive performance by determining expected loss (EL), cost of misclassification, profit/loss-curves and decision thresholds that maximize the lending results. This study shows that digital tools contribute to the reduction of the losses associated with defaults, the enhancement of the quality of loan portfolios, and allow advisors to make more efficient and consistent decisions by connecting the model performance to the quantifiable financial gains. The results also indicate that the implementation of digital tools in the advisory processes not only improves risk prevention but also increases operational efficiency through the automation of redundant activities, minimization of human biases, and standardization of evaluation processes. To ensure that financial institutions evaluate their digital-tool investment based on the long-term economic benefits and ease of implementation, a techno-economic evaluation framework is suggested. Altogether, the paper demonstrates the strategic importance of digital decision-support systems in the contemporary financial and risk consultations and presents empirical data on how the system implementation leads to technological dominance and considerable economic benefits [2]. This study will add to the dynamic discussion on the topic of digital finance by providing an effective framework of assessing the influence of digital tools on performance, cost-effectiveness, and risk outcomes.